Bias and Fairness in Artificial Intelligence

Introduction

Bias and fairness are critical topics in artificial intelligence and machine learning. A biased AI model can lead to unfair, discriminatory, or unethical outcomes. Fairness ensures that AI systems make decisions that are consistent, transparent, and equal for all individuals or groups.

بالعربية المغربية (الدارجة): الانحياز والإنصاف فـالذكاء الاصطناعي مواضيع مهمة بزاف. النموذج المنحاز يقدر يعطي قرارات ظالمة أو تمييزية. الإنصاف كيعني النموذج يخدم بعدالة ويعامل الناس كاملين بنفس الطريقة.

What is Bias in AI?

Bias happens when an AI system systematically favors or disadvantages certain groups. It can come from data, algorithms, or human choices during model design.

• Data Bias: When the dataset does not represent all groups fairly.
• Label Bias: When labels reflect human prejudice or errors.
• Algorithmic Bias: When the model amplifies existing unfair patterns.

بالعربية المغربية: الانحياز كيتوقع ملي البيانات ما كتمثلش الجميع، ولا ملي التصنيفات فيها أخطاء بشرية، ولا ملي الخوارزمية كتزيد فالتمييز اللي كان من قبل.

Fairness in Machine Learning

Fairness means that predictions are not dependent on sensitive attributes like gender, race, or age. There are different definitions of fairness, including:

• Demographic Parity: Each group gets positive predictions at similar rates.
• Equal Opportunity: True positive rates are equal across groups.
• Equalized Odds: Both true positive and false positive rates are equal across groups.

بالعربية المغربية: الإنصاف كيعني القرار ما يكونش مرتبط بالعمر ولا بالجنس ولا بالأصل. كاين بزاف ديال طرق باش نقيسو الإنصاف، بحال المساواة فالنسب أو ففرص النجاح.

Python Example: Detecting Bias in a Model

import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score

# Sample dataset
data = pd.DataFrame({
    "age": [22, 45, 35, 26, 50, 29],
    "gender": ["male", "female", "female", "male", "female", "male"],
    "income": [30000, 60000, 55000, 35000, 62000, 40000],
    "approved": [0, 1, 1, 0, 1, 0]
})

# Encode gender as numeric
data["gender_num"] = data["gender"].map({"male": 0, "female": 1})

X = data[["age", "gender_num", "income"]]
y = data["approved"]

model = LogisticRegression()
model.fit(X, y)
predictions = model.predict(X)

data["predicted"] = predictions
accuracy = accuracy_score(y, predictions)

print("Model Accuracy:", accuracy)
print("\nPredictions by gender:")
print(data.groupby("gender")[["predicted"]].mean())

Explanation of the Example

• The dataset includes features like age, gender, and income.
• The model predicts loan approval.
• By comparing average predictions by gender, we can check if the model favors one group.

بالعربية المغربية: هاد الكود كيدرب نموذج بسيط باش يتوقع القبول ديال القرض. من بعد كنشوفو واش النتيجة كتميل لجنس معين، باش نعرفو واش النموذج فيه انحياز.

Mitigating Bias in AI Models

• Preprocessing: Balance datasets before training (e.g., re-sampling, re-weighting).
• In-processing: Modify learning algorithms to reduce bias (e.g., fairness constraints).
• Post-processing: Adjust predictions after training to improve fairness.

بالعربية المغربية: باش نصلحو الانحياز نقدر نعدلو البيانات قبل التدريب، ولا نبدلو الطريقة اللي كيتعلم بها النموذج، أو نصححو النتائج من بعد التدريب.

Example: Balancing Data Before Training

from sklearn.utils import resample

# Suppose 'female' group is smaller
female_data = data[data.gender == "female"]
male_data = data[data.gender == "male"]

# Resample females to balance
female_upsampled = resample(female_data, replace=True, n_samples=len(male_data), random_state=42)
balanced_data = pd.concat([male_data, female_upsampled])

print("Data balanced successfully:")
print(balanced_data.gender.value_counts())

Best Practices for Fair AI

• Use diverse and representative datasets.
• Continuously monitor models after deployment.
• Collaborate with ethicists and domain experts.
• Report model fairness metrics in documentation.
• Test for bias before releasing an AI product.

بالعربية المغربية: باش نضمنو الإنصاف، خاص البيانات تكون متنوعة، ونراقبو النموذج حتى بعد الإطلاق، ونتأكدو دايمًا من التوازن فالنتائج.

Tools for Bias and Fairness Analysis

Tool	Purpose
AI Fairness 360 (IBM)	Detect and mitigate bias in datasets and models.
Fairlearn (Microsoft)	Measure and reduce unfairness in machine learning.
What-If Tool (Google)	Visualize model predictions and fairness metrics.
SHAP / LIME	Explain model decisions and feature influence.

بالعربية المغربية: كاينين بزاف ديال الأدوات اللي كتعاون فالكشف والتصحيح ديال الانحياز، بحال AI Fairness 360 وFairlearn وWhat-If Tool.

10 Exercises for Practice

1. Define bias and fairness in AI using your own words.
2. List three types of bias and provide an example for each.
3. Use the sample Python code to check if your model is biased by gender.
4. Add a new sensitive attribute (like age) and measure fairness again.
5. Balance your dataset using re-sampling and compare results.
6. Install and use AI Fairness 360 to detect bias in your model.
7. Explain the difference between preprocessing and post-processing techniques.
8. Apply SHAP or LIME to explain model decisions on sensitive features.
9. Discuss how bias affects AI systems in healthcare or finance.
10. Write a short report suggesting how to design fair AI systems.